Implement assign_defaults

Author: basnijholt

adaptive/learner/average_learner.py

@@ -9,7 +9,7 @@
 from adaptive.learner.base_learner import BaseLearner
 from adaptive.notebook_integration import ensure_holoviews
 from adaptive.types import Float, Real
-from adaptive.utils import cache_latest, default_parameters
+from adaptive.utils import assign_defaults, cache_latest
 
 try:
     import pandas
@@ -89,8 +89,7 @@ def to_dataframe(
             raise ImportError("pandas is not installed.")
         df = pandas.DataFrame(sorted(self.data.items()), columns=[seed_name, y_name])
         if with_default_function_args:
-            defaults = default_parameters(self.function, function_prefix)
-            df = df.assign(**defaults)
+            assign_defaults(self.function, df, function_prefix)
         return df
 
     def ask(self, n: int, tell_pending: bool = True) -> tuple[list[int], list[Float]]:

adaptive/learner/average_learner1D.py

@@ -15,7 +15,7 @@
 from adaptive.learner.learner1D import Learner1D, _get_intervals
 from adaptive.notebook_integration import ensure_holoviews
 from adaptive.types import Real
-from adaptive.utils import default_parameters
+from adaptive.utils import assign_defaults
 
 try:
     import pandas
@@ -171,8 +171,7 @@ def to_dataframe(
             columns = [seed_name, x_name, y_name]
         df = pandas.DataFrame(data, columns=columns)
         if with_default_function_args:
-            defaults = default_parameters(self.function, function_prefix)
-            df = df.assign(**defaults)
+            assign_defaults(self.function, df, function_prefix)
         return df
 
     def ask(self, n: int, tell_pending: bool = True) -> tuple[Points, list[float]]:

adaptive/learner/integrator_learner.py

@@ -13,7 +13,7 @@
 from adaptive.learner import integrator_coeffs as coeff
 from adaptive.learner.base_learner import BaseLearner
 from adaptive.notebook_integration import ensure_holoviews
-from adaptive.utils import cache_latest, default_parameters, restore
+from adaptive.utils import assign_defaults, cache_latest, restore
 
 try:
     import pandas
@@ -569,8 +569,7 @@ def to_dataframe(
             raise ImportError("pandas is not installed.")
         df = pandas.DataFrame(sorted(self.data.items()), columns=[x_name, y_name])
         if with_default_function_args:
-            defaults = default_parameters(self.function, function_prefix)
-            df = df.assign(**defaults)
+            assign_defaults(self.function, df, function_prefix)
         return df
 
     def _get_data(self):

adaptive/learner/learner1D.py

@@ -16,7 +16,7 @@
 from adaptive.learner.triangulation import simplex_volume_in_embedding
 from adaptive.notebook_integration import ensure_holoviews
 from adaptive.types import Float, Int, Real
-from adaptive.utils import cache_latest, default_parameters
+from adaptive.utils import assign_defaults, cache_latest
 
 try:
     import pandas
@@ -338,10 +338,11 @@ def to_dataframe(
     ) -> pandas.DataFrame:
         if not with_pandas:
             raise ImportError("pandas is not installed.")
-        df = pandas.DataFrame(sorted(self.data.items()), columns=[x_name, y_name])
+        xs, ys = zip(*sorted(self.data.items())) if self.data else ([], [])
+        df = pandas.DataFrame(xs, columns=["x"])
+        df[y_name] = ys
         if with_default_function_args:
-            defaults = default_parameters(self.function, function_prefix)
-            df = df.assign(**defaults)
+            assign_defaults(self.function, df, function_prefix)
         return df
 
     @property

adaptive/learner/learner2D.py

@@ -11,7 +11,7 @@
 from adaptive.learner.base_learner import BaseLearner
 from adaptive.learner.triangulation import simplex_volume_in_embedding
 from adaptive.notebook_integration import ensure_holoviews
-from adaptive.utils import cache_latest, default_parameters
+from adaptive.utils import assign_defaults, cache_latest
 
 try:
     import pandas
@@ -406,8 +406,7 @@ def to_dataframe(
         data = sorted((x, y, z) for (x, y), z in self.data.items())
         df = pandas.DataFrame(data, columns=[x_name, y_name, z_name])
         if with_default_function_args:
-            defaults = default_parameters(self.function, function_prefix)
-            df = df.assign(**defaults)
+            assign_defaults(self.function, df, function_prefix)
         return df
 
     def _scale(self, points):

adaptive/utils.py

@@ -109,3 +109,9 @@ def default_parameters(function, function_prefix="", start_index=1):
         if v.default != inspect._empty and i >= start_index
     }
     return defaults
+
+
+def assign_defaults(function, df, function_prefix="", start_index=1):
+    defaults = default_parameters(function, function_prefix, start_index)
+    for k, v in defaults.items():
+        df[k] = len(df) * [v]